AIMC Topic: Galvanic Skin Response

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Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals.

Sensors (Basel, Switzerland) Mar 30, 2021
Mental stress can lead to traffic accidents by reducing a driver's concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers' stress in advance to prevent dangerous situations increased. Thus, we propose...
Heart Rate Automobile Driving Galvanic Skin Response Neural Networks, Computer Accidents, Traffic
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Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network.

Journal of medical systems Mar 4, 2021
In this work, an attempt has been made to classify emotional states using electrodermal activity (EDA) signals and multiscale convolutional neural networks. For this, EDA signals are considered from a publicly available "A Dataset for Emotion Analysi...
Emotions Arousal Galvanic Skin Response Humans Neural Networks, Computer Learning
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Using Machine Learning to Train a Wearable Device for Measuring Students' Cognitive Load during Problem-Solving Activities Based on Electrodermal Activity, Body Temperature, and Heart Rate: Development of a Cognitive Load Tracker for Both Personal and Classroom Use.

Sensors (Basel, Switzerland) Aug 27, 2020
Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that ...
Humans Cognition Heart Rate Body Temperature Machine Learning Students Wearable Electronic Devices Problem Solving Galvanic Skin Response
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Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study.

Sensors (Basel, Switzerland) Aug 14, 2020
Process model comprehension is essential in order to understand the five Ws (i.e., who, what, where, when, and why) pertaining to the processes of organizations. However, research in this context showed that a proper comprehension of process models o...
Machine Learning Feasibility Studies Galvanic Skin Response Humans
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CNN and LSTM-Based Emotion Charting Using Physiological Signals.

Sensors (Basel, Switzerland) Aug 14, 2020
Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance impr...
Humans Galvanic Skin Response Neural Networks, Computer Electrocardiography Emotions Electroencephalography Arousal
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Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity.

International journal of neural systems Jun 5, 2020
Early detection of stress condition is beneficial to prevent long-term mental illness like depression and anxiety. This paper introduces an accurate identification of stress/calm condition from electrodermal activity (EDA) signals. The acquisition of...
Galvanic Skin Response Electrodiagnosis Stress, Psychological Wearable Electronic Devices Support Vector Machine Humans Adult Deep Learning
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Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.

Ergonomics May 7, 2020
This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experi...
Heart Rate Task Performance and Analysis Galvanic Skin Response Electroencephalography Electromyography Workload Male Surveys and Questionnaires Humans Machine Learning Computer Simulation Young Adult
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Improving energy expenditure estimates from wearable devices: A machine learning approach.

Journal of sports sciences Apr 6, 2020
A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of act...
Body Mass Index Body Composition Walking Activities of Daily Living Heart Rate Energy Metabolism Male Middle Aged Exercise Algorithms Female Adult Galvanic Skin Response Jogging Calorimetry, Indirect Sedentary Behavior Body Temperature Bicycling Accelerometry Fitness Trackers Humans Machine Learning
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Recognition of Negative Emotion using Long Short-Term Memory with Bio-Signal Feature Compression.

Sensors (Basel, Switzerland) Jan 20, 2020
Negative emotion is one reason why stress causes negative feedback. Therefore, many studies are being done to recognize negative emotions. However, emotion is difficult to classify because it is subjective and difficult to quantify. Moreover, emotion...
Emotions Signal Processing, Computer-Assisted Skin Temperature Galvanic Skin Response Humans Neural Networks, Computer Adult Male Electrocardiography
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An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy.

Sensors (Basel, Switzerland) Jan 15, 2020
In this paper, we investigate various machine learning classifiers used in our Virtual Reality (VR) system for treating acrophobia. The system automatically estimates fear level based on multimodal sensory data and a self-reported emotion assessment....
Deep Learning Galvanic Skin Response Humans Electroencephalography Adult Young Adult Heart Rate Signal Processing, Computer-Assisted Anxiety Phobic Disorders Diagnosis, Computer-Assisted Fear
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